GapminderData <- read_csv(file = Gapminder_Filelink) %>%
as_tibble(show_col_types = FALSE) %>%
select(-`...1`)
What we see here is the Gapminder dataset (even though it says it’s cleaned it’s not….). This dataset details various metrics, ranging from economic to agriculture, that describes specific countries within the world over time.
filtered_year = 1962
GapminderFilteredYear <- GapminderData %>%
dplyr::filter(Year == filtered_year)
ScatterPlot <- GapminderFilteredYear %>%
ggplot(., aes(x = gdpPercap,
y = `CO2 emissions (metric tons per capita)`)) +
geom_point() +
theme_classic()
## Warning: Removed 151 rows containing missing values (geom_point).
From our dataset, we can see that there is a positively linear relationship between CO2 emissions and GDP per capita. Now lets investigate further on how strong the correlation is based on the pearson correlation (R) coefficient.
test = "pearson"
rm_na = "complete.obs"
pearson_corr = cor(GapminderFilteredYear$`CO2 emissions (metric tons per capita)`,
GapminderFilteredYear$gdpPercap,
method = test,
use = rm_na) * 100
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 151 rows containing non-finite values (stat_smooth).
## Warning: Removed 151 rows containing non-finite values (stat_cor).
## Warning: Removed 151 rows containing missing values (geom_point).
## The pearson correlation coefficient between CO2 emissions and GDP per capita is 92.6%
From what we can see here, the pearson correlation coefficient is approximately 92.61%, meaning that there is a strong positive correlation between CO2 emissions and GDP per capita in all countries in the year of 1962. In addition, the p-value (2.2 * 10^-6) is less than 0.05, meaning that the correlation of the two variables are significant to one another. Now lets take a look at all years and see which has the highest pearson correlation coefficient.
test = "pearson"
rm_na = "complete.obs"
CO2_year = vector(mode = "list")
gdpPercap_year = vector(mode = "list")
PearsonCorrYears = vector(mode = "list")
YearChartoNum = vector(mode = "list")
GapminderYear = GapminderData %>% #selecting the all the unique years iteration
select(Year) %>%
unique() %>%
pull() %>%
as.character() #For names in the list
PearsonCorrYears = GapminderYear %>% #Make into a list by iterating through the years
sapply(.,
USE.NAMES = TRUE,
simplify = FALSE,
function(year){
YearChartoNum[[year]] = year %>% #Convert characters to numeric values
as.numeric()
CO2_year[[year]] = GapminderData %>% #list for the CO2 emissions by year
filter(Year == YearChartoNum[[year]]) %>%
select(`CO2 emissions (metric tons per capita)`) %>%
pull()
gdpPercap_year[[year]] = GapminderData %>% #list for the GDP per capita by
filter(Year == YearChartoNum[[year]]) %>% #year
select(gdpPercap) %>%
pull()
cor(x = GapminderData %>% #Pearson Correlation coefficient iterated by year
filter(Year == YearChartoNum[[year]]) %>%
select(`CO2 emissions (metric tons per capita)`) %>%
pull(),
y = gdpPercap_year[[year]],
method = test,
use = rm_na)
}) %>% unlist()
## 1967 1962 1972 1982 1987 1992 1997 2002
## 0.9387918 0.9260817 0.8428986 0.8166384 0.8095531 0.8094316 0.8081396 0.8006421
## 1977 2007
## 0.7928336 0.7204169
After iterating over the years in the Gapminder dataset, we can see that the highest Pearson correlation coefficient occurs in 1967 suggesting that year has the strongest correlation (93.88%) between CO2 emissions and GDP per capita. Now lets filter the Gapminder dataset again with that year and plot a scatterplot through plotly.
PearsonCorrMaxYear = PearsonCorrYears[which.max(PearsonCorrYears)] %>%
names() %>%
as.numeric() #Finding the max year for the analysis
GapminderFilteredMax = GapminderData %>% ##Filter by year with the highest Pearson
filter(Year == PearsonCorrMaxYear) ##correlation coefficient of CO2 and GDP
GapminderMaxplot = GapminderFilteredMax %>% ## ggplot implementation
ggplot(., aes(x =`CO2 emissions (metric tons per capita)`,
y = gdpPercap,
size = pop)) +
geom_point() + theme_classic()
Here is the plotly implementation of the Gapminder dataset during the year of 1962. The scatterplot is interactive and you can see the different values (gdpPercap, CO2 emissions) in each point of the plot.
GapminderContinentEnergyUse <- GapminderData %>%
select(continent,`Energy use (kg of oil equivalent per capita)`) %>%
na.omit()
ggboxplot(GapminderContinentEnergyUse,
x = "continent",
y = "Energy use (kg of oil equivalent per capita)",
color = "continent",
add = "jitter",
shape = "continent")
lm(formula = `Energy use (kg of oil equivalent per capita)` ~ continent,
data = GapminderContinentEnergyUse) %>% summary()
##
## Call:
## lm(formula = `Energy use (kg of oil equivalent per capita)` ~
## continent, data = GapminderContinentEnergyUse)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2796.0 -1107.5 -349.1 276.8 12904.4
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 698.5 137.2 5.090 4.42e-07 ***
## continentAmericas 1005.1 196.9 5.105 4.10e-07 ***
## continentAsia 1168.8 197.7 5.911 4.93e-09 ***
## continentEurope 2447.5 183.0 13.377 < 2e-16 ***
## continentOceania 3281.8 454.1 7.227 1.11e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1936 on 843 degrees of freedom
## Multiple R-squared: 0.1963, Adjusted R-squared: 0.1924
## F-statistic: 51.46 on 4 and 843 DF, p-value: < 2.2e-16
GapminderPopDensity <- GapminderData %>%
select(`Country Name`,
`Population density (people per sq. km of land area)`,
Year)
GapminderPopDensity %>%
ggplot(.,
aes(x = Year,
y = `Population density (people per sq. km of land area)`,
color = `Country Name`)) +
geom_point()
## Warning: Removed 49 rows containing missing values (geom_point).